\(k\) -Nearest Neighbors (\(k\) NN) is a classical non-parametric ML algorithm,
where
a data point label is chosen by selecting the plurality class of its \(k\) 
closest neighbors in the training set.
Outside of classification, many algorithms construct a nearest neighbor graph (connecting each data point to its closest neighbors) as a subroutine.
Examples include ISOMAP ([1]}), persistent homology ([2]}, [3]}), and various spectral clustering algorithms ([4]}).
